A set of members comprising a group of collaborators can be selected using a variety of criteria. For example, the members might be selected based on members' shared interests or respective skill sets. In many industries today, collaboration group members will often interact over a set of computer-readable content objects (e.g., text files, spreadsheets, mixed text and graphics documents, programming code files, etc.) as they work together to achieve a common goal.
Modern distributed computing and storage platforms (e.g., cloud-based content management platforms) have evolved to facilitate secure sharing of large volumes of such content among trusted collaborators that are geographically dispersed and/or whom may or may not know one another. For example, a large content owner (e.g., enterprise) with thousands of global users (e.g., employees) and many terabytes of content might use a cloud-based content management platform to efficiently and securely facilitate content access to various individual users and/or collaborative groups of users. The constituency of the set of individual users can be very dynamic (e.g., employees come and go, change roles, etc.) as can the content itself (e.g., as content objects are added, deleted, edited, reorganized, etc.). Further, the number of collaborating users, and the number of content objects supported or subsumed by the distributed computing and storage platforms continue to scale to ever larger numbers.
Unfortunately, identification of the most meaningful collaboration groups in such dynamic shared content environments presents many computing challenges. As an example, a user “u1” in San Francisco who is interacting with files “f1” and “f5” might not know of the possible synergetic benefits that can be achieved by joining a collaboration group with a user “u3” and (for example) a newly hired user “u9” in Seoul who just began interacting with files “f1” and “f5”. Legacy approaches have several deficiencies that demand improvements in order to be used in modern collaboration systems. Specifically, in environments comprising a large number of users that continually interact with a large number of content objects, the formation of collaboration clusters using legacy techniques consume an inordinate amount of computing resources, storage resources, and networking resources. Moreover, legacy techniques fail to facilitate identification of collaboration clusters that pertains to a particular reason or component of a collaboration cluster. Therefore, what is needed are technological solutions that improve over legacy techniques and/or over other considered approaches that address problems attendant to real-time identification and scoring of collaboration groups that remain relevant even as time progresses.
Some of the approaches described in this background section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.